An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots

Mantegazza, Dario; Giusti, Alessandro; Gambardella, Luca Maria; Guzzi, Jérôme

doi:10.1109/lra.2022.3192794

Cited by 9 publications

(3 citation statements)

References 30 publications

(53 reference statements)

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“…Therefore, in many cases, supervised learning has shown limited use in cybersecurity systems. The use of anomaly detection for mobile robots using a limited abnormal system operation dataset to train the detection algorithm is investigated in [39]. In recent years, unsupervised or semisupervised anomaly detection algorithms have become more widely used in anomaly detection.…”

Section: Deep Learning-based Algorithms For Anomaly Detectionmentioning

confidence: 99%

Deep Learning of Sensor Data in Cybersecurity of Robotic Systems: Overview and Case Study Results

Szynkiewicz,

Niewiadomska-Szynkiewicz,

Lis

2023

Electronics

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Recent technological advances have enabled the development of sophisticated robotic and sensor systems monitored and controlled by algorithms based on computational intelligence. The deeply intertwined and cooperating devices connected to the Internet and local networks, usually through wireless communication, are increasingly used in systems deployed among people in public spaces. The challenge is to ensure that physical and digital components work together securely, especially as the impact of cyberattacks is significantly increasing. The paper addresses cybersecurity issues of mobile service robots with distributed control architectures. The focus is on automatically detecting anomalous behaviors possibly caused by cyberattacks on onboard and external sensors measuring the robot and environmental parameters. We provide an overview of the methods and techniques for protecting robotic systems. Particular attention is paid to our technique for anomaly detection in a service robot’s operation based on sensor readings and deep recurrent neural networks, assuming that attacks result in the robot behaving inconsistently. The paper presents the architecture of two artificial neural networks, their parameters, and attributes based on which the potential attacks are identified. The solution was validated on the PAL Robotics TIAGo robot operating in the laboratory and replicating a home environment. The results confirm that the proposed system can effectively support the detection of computer threats affecting the sensors’ measurements and, consequently, the functioning of a service robotic system.

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Section: Deep Learning-based Algorithms For Anomaly Detectionmentioning

confidence: 99%

Deep Learning of Sensor Data in Cybersecurity of Robotic Systems: Overview and Case Study Results

Szynkiewicz,

Niewiadomska-Szynkiewicz,

Lis

2023

Electronics

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“…Unsupervised learning techniques are harnessed for anomaly detection, allowing robots to identify deviations from expected behavior [158]. One-Class SVMs (Support Vector Machines) [159], Autoencoders [160] and VAEs [161,162] are employed to learn the normal data distribution and subsequently detect anomalies. This is crucial for applications like fault detection in industrial robotics or identifying unfamiliar objects in a scene.…”

Section: Anomaly Detectionmentioning

confidence: 99%

Advanced Power Converters and Learning in Diverse Robotic Innovation: A Review

Singh,

Kurukuru,

Khan

2023

Energies

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This paper provides a comprehensive review of the integration of advanced power management systems and learning techniques in the field of robotics. It identifies the critical roles these areas play in reshaping the capabilities of robotic systems across diverse applications. To begin, it highlights the significance of efficient power usage in modern robotics. The paper explains how advanced power converters effectively control voltage, manage current and shape waveforms, thereby optimizing energy utilization. These converters ensure that robotic components receive the precise voltage levels they require, leading to improved motor performance and enabling precise control over motor behavior. Consequently, this results in extended operational times and increased design flexibility. Furthermore, the review explores the integration of learning approaches, emphasizing their substantial impact on robotic perception, decision-making and autonomy. It discusses the application of techniques such as reinforcement learning, supervised learning and unsupervised learning, showcasing their applications in areas like object recognition, semantic segmentation, sensor fusion and anomaly detection. By utilizing these learning methods, robots become more intelligent, adaptable and capable of autonomous operation across various domains. By examining the interaction between advanced power management and learning integration, this review anticipates a future where robots operate with increased efficiency, adapt to various tasks and drive technological innovation across a wide range of industries.

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“…Considered anomalies include presence of humans, unexpected objects on the floor, defects to the robot. Preliminary versions of the dataset are used in [1 , 3] . This version is available at [12]…”

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confidence: 99%